---
title: Saldae Analytics Q1
author: Anis
date: 2022-06-11 13:56
output:
  flexdashboard::flex_dashboard:
    orientation: columns
    social: menu
    theme: bootstrap
    mathjax: ~
    favicon: saldae_logo.png
    source_code: embed
params:
  sald_explor_chart: NA
  sald_predict_chart: NA
  sald_tisefka: NA
  sald_predict_values: NA
  sald_predict_comment: NA
  sald_introduction: NA
  sald_report_asezwer: NA
---
# Saldae {data-navmenu="Intro"}

Row {data-width=200}
-----------------------------------------------------------------------
### Analysis Summary {data-commentary-height=200}

```{r,echo=FALSE,warning=FALSE,results='asis'}

h4(params$sald_report_asezwer)
```

***

```{r,echo=FALSE,warning=FALSE}
future_key_figures <- unlist(params$sald_predict_values$key_figures%>%purrr::map(~.x[[1]]))
future_key_figures <- data.frame(attribute = names(future_key_figures),forecast =future_key_figures)


```

Row {data-width=500}
-----------------------------------------------------------------------
### Key Figures ( Prediction)
```{r,echo=FALSE,warning=FALSE}


DT::datatable(future_key_figures,
              extensions = c('Buttons','Scroller'), options = list(
                deferRender = TRUE,
              scrollY = 200,
              scroller = TRUE,
              dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
  )
)
```


### Key figures (contribution)
```{r,echo=FALSE,warning=FALSE}
future_key_figures_pie <- future_key_figures%>%plotly::plot_ly( labels  = ~attribute, values= ~forecast, type = 'pie')

future_key_figures_pie
```



Row
-----------------------------------------------------------------------
### Intro {data-commentary-height=400}

This is a template of interactive reporting output generated by [Saldae Analytics Platform](https://saldae-analytics.shinyapps.io/saldae-analytics-platform/).
If you want to learn more about us and our service please visit our website: 

[https://www.fairanalytics.net/](https://www.fairanalytics.net/)

*Total Sales of Beers in the following cities next month :* 

```{r,echo=FALSE,warning=FALSE}
future_key_figures_bar <- future_key_figures%>%plotly::plot_ly( y = ~attribute, x = ~forecast, type = 'bar',color = ~attribute, orientation = 'h')
future_key_figures_bar

```


***

### Useful concepts {data-commentary-height=300}


**Forecasting:**

Forecasting is required in many situations. Stocking an inventory may require forecasts of demand months in advance. Telecommunication routing requires traffic forecasts a few minutes ahead. Whatever the circumstances or time horizons involved, forecasting is an important aid in effective and efficient planning

***


`r names(params$sald_explor_chart)[[1]]`
=======================================================================

Row {data-width=200}
-----------------------------------------------------------------------


### `r paste(names(params$sald_explor_chart)[[1]] ,":",params$sald_predict_values$key_metric)`


```{r}
my_key_figure <- params$sald_predict_values$key_figures[[1]]
flexdashboard::valueBox(my_key_figure,color = "brown", icon = "fa-euro-sign")
```

### Summary {data-commentary-height=100}

```{r,warning=FALSE,echo=FALSE}
h5(params$sald_predict_comment[[1]])
```

***

Row {.tabset}
-----------------------------------------------------------------------

### Forecast Chart

```{r, warning=FALSE,fig.height=6,echo=FALSE}
params$sald_explor_chart[[1]]

```


### Forecast Table

```{r, warning=FALSE,fig.height=4,echo=FALSE}

DT::datatable(params$sald_predict_chart[[1]],
              extensions = c('Buttons','Scroller'), options = list(
                deferRender = TRUE,
              scrollY = 400,
              scroller = TRUE,
              dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
  )
)
```

### Anomalies 

```{r ,warning=FALSE,fig.height=4,message=FALSE,echo=FALSE}
tisefka <- params$sald_tisefka%>%select(date,names(params$sald_explor_chart)[1])
tisefka2 <<- tisefka
tisefka%>%
  SaldaeDataExplorer::anomaly_detection_yiwen()%>%
  SaldaeDataExplorer::SA_anomaly_charter(target_variable = names(params$sald_explor_chart)[1])
```


`r names(params$sald_explor_chart)[[2]]`
=======================================================================

Row {data-width=200}
-----------------------------------------------------------------------


### `r paste(names(params$sald_explor_chart)[[2]] ,":",params$sald_predict_values$key_metric)`


```{r}
my_key_figure <- params$sald_predict_values$key_figures[[2]]
flexdashboard::valueBox(my_key_figure,color = "brown", icon = "fa-euro-sign")
```

### Summary {data-commentary-height=100}

```{r,warning=FALSE,echo=FALSE}
h5(params$sald_predict_comment[[2]])
```

***

Row {.tabset}
-----------------------------------------------------------------------

### Forecast Chart

```{r, warning=FALSE,fig.height=6,echo=FALSE}
params$sald_explor_chart[[2]]

```


### Forecast Table

```{r, warning=FALSE,fig.height=4,echo=FALSE}

DT::datatable(params$sald_predict_chart[[2]],
              extensions = c('Buttons','Scroller'), options = list(
                deferRender = TRUE,
              scrollY = 400,
              scroller = TRUE,
              dom = 'Bfrtip',
    buttons = c('copy', 'csv', 'excel', 'pdf', 'print')
  )
)
```

### Anomalies 

```{r ,warning=FALSE,fig.height=4,message=FALSE,echo=FALSE}
tisefka <- params$sald_tisefka%>%select(date,names(params$sald_explor_chart)[2])
tisefka2 <<- tisefka
tisefka%>%
  SaldaeDataExplorer::anomaly_detection_yiwen()%>%
  SaldaeDataExplorer::SA_anomaly_charter(target_variable = names(params$sald_explor_chart)[2])
```